In [1]}, the authors focused on the problem of lifelong learning and how an agent learns the optimal policy of a particular MDP using the information of a sequence of MDPs from the same distribution. Specifically, the problem in this work is to search for an optimal exploration policy which an agent follows during exploration in the environment. Each agent maintains two policies: an exploitation policy which is task-specific and an exploration policy which is shared between all the MDPs drawn from the same distribution. At each timestep, each of the two policies provide an action and the selected action is determined by \(\epsilon \) -greedy algorithm. The exploration policy receives the same reward as the exploitation policy and a variant of policy gradient algorithm (REINFORCE or PPO) is used to update the policy. This approach is experimented on some typical RL problem classes such as Pole Balancing.
